Overview

Dataset statistics

Number of variables12
Number of observations6362620
Missing cells0
Missing cells (%)0.0%
Duplicate rows543
Duplicate rows (%)< 0.1%
Total size in memory497.6 MiB
Average record size in memory82.0 B

Variable types

NUM6
BOOL4
CAT2

Warnings

Dataset has 543 (< 0.1%) duplicate rows Duplicates
nameDest_char is highly correlated with typeHigh correlation
type is highly correlated with nameDest_charHigh correlation
amount is highly skewed (γ1 = 30.99394948) Skewed
FluxOrig is highly skewed (γ1 = -24.63052048) Skewed
FluxDest is highly skewed (γ1 = 32.91634067) Skewed
oldbalanceOrg has 2102449 (33.0%) zeros Zeros
oldbalanceDest has 2704388 (42.5%) zeros Zeros
FluxOrig has 2089037 (32.8%) zeros Zeros
FluxDest has 2317292 (36.4%) zeros Zeros

Reproduction

Analysis started2020-10-25 19:51:45.384619
Analysis finished2020-10-25 19:57:45.182772
Duration5 minutes and 59.8 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

step
Real number (ℝ≥0)

Distinct743
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.3972456
Minimum1
Maximum743
Zeros0
Zeros (%)0.0%
Memory size48.5 MiB
2020-10-25T16:57:45.297887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q1156
median239
Q3335
95-th percentile490
Maximum743
Range742
Interquartile range (IQR)179

Descriptive statistics

Standard deviation142.331971
Coefficient of variation (CV)0.5847723161
Kurtosis0.329070555
Mean243.3972456
Median Absolute Deviation (MAD)92
Skewness0.3751768885
Sum1548644183
Variance20258.38998
MonotocityIncreasing
2020-10-25T16:57:45.468551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19513520.8%
 
18495790.8%
 
187490830.8%
 
235474910.7%
 
307469680.7%
 
163463520.7%
 
139460540.7%
 
403451550.7%
 
43450600.7%
 
355447870.7%
 
Other values (733)589073992.6%
 
ValueCountFrequency (%) 
12708< 0.1%
 
21014< 0.1%
 
3552< 0.1%
 
4565< 0.1%
 
5665< 0.1%
 
ValueCountFrequency (%) 
7438< 0.1%
 
74214< 0.1%
 
74122< 0.1%
 
7406< 0.1%
 
73910< 0.1%
 

type
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
CASH_OUT
2237500 
PAYMENT
2151495 
CASH_IN
1399284 
TRANSFER
532909 
DEBIT
 
41432
ValueCountFrequency (%) 
CASH_OUT223750035.2%
 
PAYMENT215149533.8%
 
CASH_IN139928422.0%
 
TRANSFER5329098.4%
 
DEBIT414320.7%
 
2020-10-25T16:57:45.637713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-25T16:57:45.727801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:57:45.844910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length7.422395963
Min length5

amount
Real number (ℝ≥0)

SKEWED

Distinct5316900
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179861.9035
Minimum0
Maximum92445516.64
Zeros16
Zeros (%)< 0.1%
Memory size48.5 MiB
2020-10-25T16:57:49.059999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2224.0995
Q113389.57
median74871.94
Q3208721.4775
95-th percentile518634.1965
Maximum92445516.64
Range92445516.64
Interquartile range (IQR)195331.9075

Descriptive statistics

Standard deviation603858.2315
Coefficient of variation (CV)3.357343715
Kurtosis1797.956705
Mean179861.9035
Median Absolute Deviation (MAD)68393.655
Skewness30.99394948
Sum1.144392945e+12
Variance3.646447637e+11
MonotocityNot monotonic
2020-10-25T16:57:49.231665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1000000032070.1%
 
1000088< 0.1%
 
500079< 0.1%
 
1500068< 0.1%
 
50065< 0.1%
 
10000042< 0.1%
 
2150037< 0.1%
 
12000029< 0.1%
 
13500020< 0.1%
 
016< 0.1%
 
Other values (5316890)635896999.9%
 
ValueCountFrequency (%) 
016< 0.1%
 
0.011< 0.1%
 
0.023< 0.1%
 
0.032< 0.1%
 
0.041< 0.1%
 
ValueCountFrequency (%) 
92445516.641< 0.1%
 
73823490.361< 0.1%
 
71172480.421< 0.1%
 
69886731.31< 0.1%
 
69337316.271< 0.1%
 

oldbalanceOrg
Real number (ℝ≥0)

ZEROS

Distinct1845844
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean833883.1041
Minimum0
Maximum59585040.37
Zeros2102449
Zeros (%)33.0%
Memory size48.5 MiB
2020-10-25T16:57:50.317707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14208
Q3107315.175
95-th percentile5823702.278
Maximum59585040.37
Range59585040.37
Interquartile range (IQR)107315.175

Descriptive statistics

Standard deviation2888242.673
Coefficient of variation (CV)3.46360618
Kurtosis32.96487854
Mean833883.1041
Median Absolute Deviation (MAD)14208
Skewness5.249136421
Sum5.305681316e+12
Variance8.341945738e+12
MonotocityNot monotonic
2020-10-25T16:57:50.484368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0210244933.0%
 
184918< 0.1%
 
133914< 0.1%
 
195912< 0.1%
 
164909< 0.1%
 
181908< 0.1%
 
109908< 0.1%
 
157902< 0.1%
 
146899< 0.1%
 
136898< 0.1%
 
Other values (1845834)425200366.8%
 
ValueCountFrequency (%) 
0210244933.0%
 
0.051< 0.1%
 
0.181< 0.1%
 
0.211< 0.1%
 
0.441< 0.1%
 
ValueCountFrequency (%) 
59585040.371< 0.1%
 
57316255.051< 0.1%
 
50399045.081< 0.1%
 
49585040.371< 0.1%
 
47316255.051< 0.1%
 

oldbalanceDest
Real number (ℝ≥0)

ZEROS

Distinct3614697
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100701.667
Minimum0
Maximum356015889.4
Zeros2704388
Zeros (%)42.5%
Memory size48.5 MiB
2020-10-25T16:57:52.624923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median132705.665
Q3943036.7075
95-th percentile5147229.713
Maximum356015889.4
Range356015889.4
Interquartile range (IQR)943036.7075

Descriptive statistics

Standard deviation3399180.113
Coefficient of variation (CV)3.088193846
Kurtosis948.6741254
Mean1100701.667
Median Absolute Deviation (MAD)132705.665
Skewness19.92175792
Sum7.003346437e+12
Variance1.155442544e+13
MonotocityNot monotonic
2020-10-25T16:57:52.795088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0270438842.5%
 
10000000615< 0.1%
 
20000000219< 0.1%
 
3000000086< 0.1%
 
4000000031< 0.1%
 
10221< 0.1%
 
19819< 0.1%
 
16018< 0.1%
 
12518< 0.1%
 
13218< 0.1%
 
Other values (3614687)365718757.5%
 
ValueCountFrequency (%) 
0270438842.5%
 
0.011< 0.1%
 
0.031< 0.1%
 
0.131< 0.1%
 
0.331< 0.1%
 
ValueCountFrequency (%) 
356015889.41< 0.1%
 
355553416.31< 0.1%
 
355381433.61< 0.1%
 
355380483.51< 0.1%
 
355185537.11< 0.1%
 

isFraud
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
0
6354407 
1
 
8213
ValueCountFrequency (%) 
0635440799.9%
 
182130.1%
 
2020-10-25T16:57:52.914204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
0
6362604 
1
 
16
ValueCountFrequency (%) 
06362604> 99.9%
 
116< 0.1%
 
2020-10-25T16:57:52.956745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

FluxOrig
Real number (ℝ)

SKEWED
ZEROS

Distinct2962285
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21230.5645
Minimum-10000000
Maximum1915267.9
Zeros2089037
Zeros (%)32.8%
Memory size48.5 MiB
2020-10-25T16:57:54.724442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-10000000
5-th percentile-74619
Q1-10150.435
median0
Q30
95-th percentile251627.927
Maximum1915267.9
Range11915267.9
Interquartile range (IQR)10150.435

Descriptive statistics

Standard deviation146643.2865
Coefficient of variation (CV)6.907177925
Kurtosis1509.981332
Mean21230.5645
Median Absolute Deviation (MAD)7273.94
Skewness-24.63052048
Sum1.350820143e+11
Variance2.150425347e+10
MonotocityNot monotonic
2020-10-25T16:57:54.906118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0208903732.8%
 
-184737< 0.1%
 
-181732< 0.1%
 
-186731< 0.1%
 
-195728< 0.1%
 
-157728< 0.1%
 
-111726< 0.1%
 
-146726< 0.1%
 
-136722< 0.1%
 
-164722< 0.1%
 
Other values (2962275)426703167.1%
 
ValueCountFrequency (%) 
-100000003< 0.1%
 
-100000005< 0.1%
 
-10000000262< 0.1%
 
-1000000012< 0.1%
 
-100000002< 0.1%
 
ValueCountFrequency (%) 
1915267.91< 0.1%
 
1821279.531< 0.1%
 
1782621.491< 0.1%
 
1781905.251< 0.1%
 
1675137.61< 0.1%
 

FluxDest
Real number (ℝ)

SKEWED
ZEROS

Distinct4011056
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124294.7317
Minimum-13060826.21
Maximum105687838.8
Zeros2317292
Zeros (%)36.4%
Memory size48.5 MiB
2020-10-25T16:57:57.417027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-13060826.21
5-th percentile-221270.2365
Q10
median0
Q3149105.425
95-th percentile559989.539
Maximum105687838.8
Range118748665
Interquartile range (IQR)149105.425

Descriptive statistics

Standard deviation812939.0811
Coefficient of variation (CV)6.540414626
Kurtosis1765.310944
Mean124294.7317
Median Absolute Deviation (MAD)62705.85
Skewness32.91634067
Sum7.908401457e+11
Variance6.608699495e+11
MonotocityNot monotonic
2020-10-25T16:57:57.591195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0231729236.4%
 
10000000136< 0.1%
 
-1000077< 0.1%
 
-500075< 0.1%
 
-1500063< 0.1%
 
-50059< 0.1%
 
-2150035< 0.1%
 
-10000035< 0.1%
 
12000027< 0.1%
 
-13500017< 0.1%
 
Other values (4011046)404480463.6%
 
ValueCountFrequency (%) 
-13060826.211< 0.1%
 
-9681485.461< 0.1%
 
-6755513.961< 0.1%
 
-6214174.811< 0.1%
 
-5353303.671< 0.1%
 
ValueCountFrequency (%) 
105687838.81< 0.1%
 
100325160.31< 0.1%
 
99045450.431< 0.1%
 
92445516.641< 0.1%
 
85923104.651< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
True
3939445 
False
2423175 
ValueCountFrequency (%) 
True393944561.9%
 
False242317538.1%
 
2020-10-25T16:57:57.703807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
False
4924370 
True
1438250 
ValueCountFrequency (%) 
False492437077.4%
 
True143825022.6%
 
2020-10-25T16:57:57.748346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

nameDest_char
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
C
4211125 
M
2151495 
ValueCountFrequency (%) 
C421112566.2%
 
M215149533.8%
 
2020-10-25T16:57:57.830927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-25T16:57:57.907499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:57:57.991081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Interactions

2020-10-25T16:55:46.271692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:55:49.030897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:55:51.375650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:55:53.727911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:55:56.279862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:55:58.722208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:01.165716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:03.549006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:05.970827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:08.277543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:10.739410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:13.153228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:15.587066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:17.986371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:20.305599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:22.626829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:25.106714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:27.526536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:29.962377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:32.356178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:34.670900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:37.005644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:39.506546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:41.933378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:44.431278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:46.898648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:49.272429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:51.638201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:54.170263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:56.652648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:56:59.119518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:57:01.601903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:57:03.967676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:57:06.334950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:57:08.869885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:57:11.335755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-25T16:57:58.111197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-25T16:57:58.321398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-25T16:57:58.531602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-25T16:57:58.747807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-25T16:57:58.941494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-25T16:57:21.544563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-25T16:57:27.103403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

steptypeamountoldbalanceOrgoldbalanceDestisFraudisFlaggedFraudFluxOrigFluxDestamount_anomaly_origamount_anomaly_destnameDest_char
01PAYMENT9839.64170136.000.000-9839.640.00FalseFalseM
11PAYMENT1864.2821249.000.000-1864.280.00FalseFalseM
21TRANSFER181.00181.000.010-181.000.00FalseTrueC
31CASH_OUT181.00181.0021182.010-181.00-21182.00FalseTrueC
41PAYMENT11668.1441554.000.000-11668.140.00FalseFalseM
51PAYMENT7817.7153860.000.000-7817.710.00FalseFalseM
61PAYMENT7107.77183195.000.000-7107.770.00FalseFalseM
71PAYMENT7861.64176087.230.000-7861.640.00FalseFalseM
81PAYMENT4024.362671.000.000-2671.000.00TrueFalseM
91DEBIT5337.7741720.0041898.000-5337.77-1549.21FalseTrueC

Last rows

steptypeamountoldbalanceOrgoldbalanceDestisFraudisFlaggedFraudFluxOrigFluxDestamount_anomaly_origamount_anomaly_destnameDest_char
6362610742TRANSFER63416.9963416.990.0010-63416.990.00FalseTrueC
6362611742CASH_OUT63416.9963416.99276433.1810-63416.9963416.99FalseFalseC
6362612743TRANSFER1258818.821258818.820.0010-1258818.820.00FalseTrueC
6362613743CASH_OUT1258818.821258818.82503464.5010-1258818.821258818.83FalseTrueC
6362614743TRANSFER339682.13339682.130.0010-339682.130.00FalseTrueC
6362615743CASH_OUT339682.13339682.130.0010-339682.13339682.13FalseFalseC
6362616743TRANSFER6311409.286311409.280.0010-6311409.280.00FalseTrueC
6362617743CASH_OUT6311409.286311409.2868488.8410-6311409.286311409.27FalseTrueC
6362618743TRANSFER850002.52850002.520.0010-850002.520.00FalseTrueC
6362619743CASH_OUT850002.52850002.526510099.1110-850002.52850002.52FalseFalseC

Duplicate rows

Most frequent

steptypeamountoldbalanceOrgoldbalanceDestisFraudisFlaggedFraudFluxOrigFluxDestamount_anomaly_origamount_anomaly_destnameDest_charcount
498387CASH_OUT10000000.0010000000.00.010-10000000.010000000.0FalseFalseC4
532617CASH_OUT10000000.0010000000.00.010-10000000.010000000.0FalseFalseC4
534646CASH_OUT10000000.0010000000.00.010-10000000.010000000.0FalseFalseC3
07PAYMENT1849.500.00.0000.00.0TrueFalseM2
18PAYMENT7759.310.00.0000.00.0TrueFalseM2
29PAYMENT2388.930.00.0000.00.0TrueFalseM2
39PAYMENT2783.830.00.0000.00.0TrueFalseM2
49PAYMENT6499.280.00.0000.00.0TrueFalseM2
59PAYMENT6755.570.00.0000.00.0TrueFalseM2
69PAYMENT10042.850.00.0000.00.0TrueFalseM2